基于指导方针,但并非无错误:人工智能为患者提供胆结石咨询的多语言风险
Olgun Erdem1, Tolga Canbak1, Aylin Acar1
1University of Health Sciences, Umraniye Training and Research Hospital, Department of General Surgery, Istanbul, Turkiye.
International journal of medical informatics
|February 14, 2026
概括
大型语言模型 (LLM) 提供健康信息,但其准确性因语言和模型而异. 来自LLM的胆结石建议可以与指南保持一致,但存在风险,特别是在土耳其语中.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 患者越来越多地使用大型语言模型 (LLM) 来获取健康信息.
- 关于准则一致性和LLM输出安全性的不确定性,特别是在多语言环境中.
- 需要对面向患者的健康咨询的LLM平台进行评估.
研究的目的:
- 评估与胆结石有关的咨询的LLM输出指南一致性和安全性.
- 为了比较不同LLM平台 (ChatGPT,Gemini,Perplexity) 的英语和土耳其语的性能.
- 在LLM答案中识别语言依赖的差异.
主要方法:
- 用土耳其语和英语对14个患者问题的横截面内容分析.
- 来自ChatGPT (GPT-4o mini),Gemini (3-flash) 和Perplexity (Sonar) 的LLM反应被两个外科医生独立评价.
- 使用EASL 2016和东京指南2018评估指南一致性;分析错误类型和响应长度.
主要成果:
- 在英语中,ChatGPT和Gemini显著优于Perplexity;土耳其语的表现差异并不显著.
- 与土耳其语相比,ChatGPT在英语中表现更好.
- 语言依赖的错误概况:土耳其语的答案往往没有充分解释,而英语的答案增加了风险;困惑的错误负担最高.
结论:
- 对于胆结石查询的LLM响应对模型和语言敏感,造成临床相关的安全风险.
- 对于医疗保健中的LLM,多语言评估标准是必不可少的.
- 阻止对LLM进行患者指导的无监督依赖,尤其是低资源语言.
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